<?xml version="1.0" encoding="UTF-8"?><item href="/people/mahdi-pedram.html" dsn="people"><first_name>Mahdi</first_name><last_name>Pedram</last_name><prefixes/><post_nominals/><title-1>Assistant Professor</title-1><title-2/><title-3/><title-4/><department>Biomedical Engineering,Computer Science and Engineering</department><expertise>Bioengineering and Health,Sensors</expertise><type>Full-Time Faculty,Courtesy Faculty</type><email>Mahdi.Pedram@unt.edu</email><phone/><image><img src="/people/images/mahdi_pedram.jpg" alt="Mahdi Pedram"/></image><office>Discovery Park F295</office><office-hours>Office Hours:<br/>Tue 1:00 - 3:00 pm</office-hours><types><type>Full-Time Faculty</type><type>Courtesy Faculty</type></types><departments><department>Biomedical Engineering</department><department>Computer Science and Engineering</department></departments><expertise-list><expertise>Bioengineering and Health</expertise><expertise>Sensors</expertise></expertise-list><main-content>Faculty Info | Website | Google Scholar | 
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Education


PhD, Washington State University, 2021Major: Computer EngineeringDissertation: Designing Resource Efficient Embedded Systems for Health Monitoring
B.S, Amirkabir University of Technology, 2014Major: Computer Engineering




Research

Dr. Pedram's research spans the fields of embedded systems, machine learning, and mobile health, focusing on developing real-time, wearable technology for health monitoring and behavior prediction. His work integrates intelligent sensors, data processing, and human-computer interaction to enhance understanding lifestyle habits and improve health outcomes. By designing and prototyping novel devices, Dr. Pedram seeks to create self-sustaining, user-friendly tools that empower individuals to manage health behaviors proactively. His multidisciplinary approach addresses critical areas like hydration, sun protection, diabetes management, and activity monitoring, utilizing advanced techniques in sensor fusion, machine learning, and privacy-preserving algorithms to provide real-time feedback and support for diverse populations, including older adults and high-risk individuals. He is interested in research in the following areas:

Embedded Systems
Health Informatics
Mobile Health (mHealth)
Smart Sensing Solutions
On-Device Processing
Power Optimization and Energy Harvesting




 
 


 
 


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